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Analysis of Contributing Factors in Crashes Involving Electric Vehicles and Vehicles with Warning Systems and Level 1 Automated Features: A State Level Analysis

Most light-duty vehicle (LDV) crashes occur due to human error. The National Highway Safety Administration (NHTSA) reports that about nine percent of fatal crashes in 2019 were distraction-affected crashes, while close to ninety-four percent of all crashes occur in part due to human error. Crash avoidance features could reduce both the frequency and severity of light and heavy-duty vehicle crashes, primarily caused by distracted driving behaviors and/or human error by assisting in maintaining control or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. Additionally, the market penetration of electric vehicles (EVs) is increasing, in turn increasing the weight and size of vehicles on the road. Although there has been research done to better understand the effects of warning and partial automation systems and EVs on traffic safety at the national level, the research team is not aware of any analysis that has been at the state or local level. While conducting research at the national level helps to inform federal policy, case studies are needed at the state and local levels to help better inform state and local policy and infrastructure investment decision-making. This project develops a replicable, open, deployable model that can: (1) identify crashes that occurred in Pennsylvania (PA) involving EVs or vehicles with warning or Level 1 systems, (2) assess the distribution of crashes involving EVs or vehicles with warning or Level 1 systems across factors such as weather conditions, vehicle speed, crash severity, pre-crash movement, facility type, and time of day, (3) estimate the relationship between contributing factors and the severity of crashes involving EVs or vehicles with warning or Level 1 features using regression analysis, and (4) make recommendations to the Pennsylvania Department of Transportation (PennDOT) and the City of Pittsburgh on policy and infrastructure improvements to improve traffic safety. The hypothesis is that current and past vehicles with warning systems and Level 1 features perform well in certain driving scenarios, facility types, and weather conditions but not others. However, there are not many frameworks and tools to help state and local agencies better understand the causes of crashes involving partially automated features, the locations of crash hotspots, and the infrastructure improvements and policies that could enhance road safety with automated vehicles (AVs). Additionally, this study should help technology providers better understand under which scenarios and conditions further testing and technology development is needed. The research team also hypothesizes that the characteristics and patterns of crashes involving EVs differ from crashes not involving this technology. Most studies that have assessed the role of warning and partial automation systems on traffic safety have focused on national-level analysis (Harper et al., 2016; Khan et al., 2019). This study builds off of previous UTC projects and add to the literature by being the first analysis the team is aware of to provide a framework for state and local agencies to assess factors contributing to crashes involving vehicles with warning systems and Level 1 features and identify locations and the infrastructure upgrades needed to support today’s sensing systems. In the first part of this project, the team will compile crash data to better understand the frequency of crashes with warning systems and Level 1 features across contributing factors (e.g., weather or vehicle speed). Second, the team will incorporate this data into a regression model to better understand the relationship between the contributing factors and the frequency and severity of crashes. Third, the team will conduct an exploratory analysis on the contributing factors for crashes involving EVs. Finally, the team will make recommendations to PennDOT and the City of Pittsburgh on policy and infrastructure improvements to improve traffic safety. To do this analysis, the team will use the PennDOT crash dataset, which contains information on all police-reported crashes of all severities for PA. In this phase of this project, the team will mainly focus on contributing factors for crashes involving vehicles with warning systems and Level 1 features and will use similar datasets to conduct a more in-depth analysis on the contributing factors for EV crashes in future iterations. Because of Tesla’s dominance in both the EV and AV market space it’s important to consider both the role that EVs and AVs play in crash safety. By focusing on both technologies independently, the team can better understand the different ways they are impacting crash safety and how to mitigate any negative effects through policy.

Language

  • English

Project

  • Status: Completed
  • Funding: $200,000.00
  • Contract Numbers:

    69A3552344811

    69A3552348316

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Principal Investigators:

    Harper, Corey

    Hendrickson, Chris

  • Start Date: 20250701
  • Expected Completion Date: 20260831
  • Actual Completion Date: 20260831
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 02002851
  • Record Type: Research project
  • Source Agency: Safety21 University Transportation Center
  • Contract Numbers: 69A3552344811, 69A3552348316
  • Files: UTC, RIP
  • Created Date: Sep 17 2026 4:19PM